The visual acuity of the eye may be measured by asking a subject to distinguish images of objects such as letters or shapes placed upon a white background. Such tests are often employed in assessing corrective lenses such as eye glasses or contact lenses. Objects within an image can typically be better distinguished from the image background if they have a distinctive luminance or colour relative to the background. For example, the relative differences in luminance can be expressed in terms of a quantity known in the art as a ‘contrast ratio’, or simply ‘contrast’. This is typically defined in terms of the difference between two luminance values divided by their sum.
Generally speaking, objects that are difficult to observe relative to their background will have a small contrast. It has been found by experiment that the eye is unable to detect objects within an image when the contrast of the object is below a threshold value, often referred to as the ‘contrast detection threshold’, or ‘contrast threshold’. The reciprocal of this minimum perceivable contrast is often referred to as the ‘contrast sensitivity’ of the eye.
In the past, in order to investigate and quantify contrast sensitivity, test images containing test patterns have been used. These have often included sinusoidal test patterns comprising a sinusoidal luminance variation extending in one dimension across the image to form stripes of continuously varying (rising and falling) luminance. For such luminance test patterns, contrast is defined simply as the amplitude of the sinusoid divided by the (uniform) mean value of the sinusoid. The threshold amount of contrast required in such a pattern for it to be reliably detected/perceived (e.g. sufficient to give a 50% detection probability) is therefore known as the contrast threshold. The contrast threshold of such a test pattern is dependent upon the wavelength of sinusoidal variation in the image (i.e. the spatial separation, transverse to the stripes, between successive luminance peaks). The reciprocal of this wavelength is known as the ‘spatial frequency’ of the pattern. Contrast sensitivity may also be measured using a non-sinusoidal luminance variation, and in such cases contrast may be defined as the difference between the maximum and minimum luminance in an image, divided by the sum of them. This is known a ‘Michelson contrast’.
Models for various aspects of contrast sensitivity exist in the prior art for ‘photopic’ luminance conditions—i.e. luminance conditions at daylight vision. These models are based on certain assumptions about the functioning of the human eye. They provide mathematical expressions for quantifying the contrast sensitivity of the eye. A central idea of such models is an assumption that contrast sensitivity is determined by noise in the visual system.
In practice, it has been found that there is not a fixed contrast threshold below which a contrast pattern cannot be detected at all, and above which the contrast pattern can always be detected. Rather, there exists a gradually increasing contrast detection probability. The contrast threshold is typically defined as the contrast at which a 50% probability of detection will exist. A contrast value that is lower than the contrast threshold would be detected with less than 50% probability. The mathematical function that describes the contrast detection probability as a function of contrast strength is generally known as the ‘psychometric function’. The statistical factors that determine the shape of the psychometric function are generally considered to be caused by noise, a part of which is internal to the visual system. One example of a psychometric function that has been successfully used in this context is a Normal probability integral which is a cumulative probability distribution function of well-known form, based on a Gaussian (“Normal”) probability density function centred on the contrast threshold value. It is a function of the value of the image contrast in question, and it rises continuously from a probability of 0.0 when that contrast is 0.0 to a value asymptotically approaching 1.0 as the contrast increases, passing through a value of 0.5 when the contrast is equal to the contrast threshold.
Experiments suggest that, under photopic conditions, the appearance of the apparent/perceived/visual contrast of two sinusoidal patterns (patterns 1 and 2) is perceived to be equal (i.e. to match) when the true/physical contrast values (C) of the images in question actually differ by the difference in their respective contrast thresholds (CT), such that:C1−C2=C1T−C2T 
This means that:C1−C1T=C2−C2T 
Therefore, the sensation evoked by physical contrast C is generally considered to be a function of its visual contrast (C−CT). The visual contrast in a sinusoidal image, at least, is considered always to be reduced, relative to its true/physical contrast, by the contrast threshold and is proportional to the true/physical contrast of the image.
Luminance levels in images play an important role in the perceived contrast of objects within that image. An image/scene viewed under differing luminance conditions is found to be perceived differently. The same physical scene seen in bright sunlight and in dusky conditions does not appear identical to the human eye. Similarly, images shown on a bright image display and on a relatively lower luminance cinema screen also differ significantly in their appearance.
Colour and contrast perception varies significantly across the range of illumination levels. The most dramatic change in vision is observed when luminance drops below 3-5 cd/m2, when the retinal cone cells steadily lose their sensitivity and visual signal is influenced by the retinal rod cells. In this, so called, ‘mesopic’ vision range, a gradual loss of acuity and colour vision occurs. This important characteristic of the visual system is rarely taken into account when reproducing colours on electronic displays. While the state-of-the-art display colourimetry is almost entirely based on the cone-mediated vision (CIE colour matching functions), a significant portion of the colour gamut in modern displays often lies in the luminance range below 3 cd/m2, which is partly mediated by rods. This is especially relevant for mobile phone displays, which can decrease their brightness down to 10-30 cd/m2 of the peak luminance to reduce power consumption. This means that in the case of a high contrast display that is dimmed, about ¾ of the perceived colour gamut cannot be accurately reproduced using traditional cone-based colorimetry.
The invention aims to address these limitations in the prior art particularly, though not exclusively, in relation to mesopic vision.